Conformer Generation with OMEGA: Learning from the Data Set and the Analysis of Failures
Conformer Generation with OMEGA: Learning from the Data Set and the Analysis of Failures
复制标题
DOI：
10.1021/ci300314k
复制
发表时间：
2012-11-01
影响因子：
5.6
通讯作者：
Nicholls, Anthony
中科院分区：
文献类型：
--
作者：
Hawkins, Paul C. D.;Nicholls, Anthony
我们最近发布了：一套高质量的验证集。用于测试变形发生器，由PDB和CSD的结构组成(Hawkins，P.C.D.等J.Chem.信息模型2010，SO，572.)，并在本出版物中测试了我们的构象生成器omega在这些集合上的性能，我们重点了解这些数据的适用性。用于验证和识别的集合；学习：从欧米茄的失败中吸取教训。我们第一次比较了我们所知道的适用属性的覆盖率：我们使用的验证数据集和父复合集之间的空间，以确定我们的数据集是否对这些属性空间进行了足够的采样。我们还介绍了扭转指纹图谱的概念，并将这种异化方法与我们在以前的出版物中使用的更传统的以图形为中心的多样化方法进行了比较，以提高我们以编程方式识别晶体构象在计算上没有很好再现的情况的能力，我们：引入了一个新的度量来进行比较。构象，RMSTAnimoto。这一新的衡量标准与我们之前发表的那些标准一起使用，以有效地识别复制失败：我们发现RMSTAnimoto在识别我们数据集中最小分子的失败方面特别有效。特别是对这些故障的性质进行分析。对于CSD的那些，进一步揭示了晶体结构中的应变问题一些残留的失效案例没有通过简单地改变omega的缺省值来解决，这给像omega这样的构象生成引擎带来了巨大的挑战，并且是进一步提高其性能的新途径的来源，而其他例子说明了对照晶体配体构象验证的陷阱，特别是那些。从PDB来的。
We recently published:a high quality validation set. for testing conformer generators, consisting of structures from both the PDB and the CSD (Hawkins, P. C. D. et al J. Chem. Inf. Model 2010, SO, 572.), and tested the performance Of our conformer generator, OMEGA, on these sets In the present publication, we focus on understanding the suitability of those data. sets for validation and identifying and;learning: from OMEGA's failures. We compare, for the first time we are aware of, the coverage of the applicable property :spaces between the validation data sets we used and the parent compound sets to determine if our data sets adequately sample these property spaces. We also introduce the concept of torsion fingerprinting and compare this method of dissimilation to the more traditional graph centric diversification methods we used in our previous publication To improve our ability to programmatically identify cases where the crystallographic conformation is not well reproduced computationally, we :introduce a new metric to compare. conformations, RMSTanimoto. This new metric is used alongside those from our previous publication to efficiently identify reproduction failures: We find RMSTanimoto to be particularly effective in identifying failures for the smallest molecules in our data Sets. Analysis of the nature of these failures, particularly. those for the CSD, sheds further light on the issue of strain in crystallographic structures Some of the residual failure cases not, resolved by simple changes in OMEGA's defaults present significant challenges to conformer generation engines like OMEGA and are a Source of new avenues to further improve their performance, while others illustrate the pitfalls of validating against crystallographic ligand conformations, particularly those. from the PDB.